system

Abstract

We provide the system. [Solution] A means for automatically collecting data on a network based on key information, A means of performing image analysis and natural language processing on collected data to extract relevant information, A means for determining the reliability of extracted information and integrating highly reliable information, A means of generating a family tree using integrated information, A means of suggesting additional relevant information based on the generated family tree, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, many people want to clearly understand their family line and roots. However, the information regarding this is vast and scattered, making it difficult for an individual to create an accurate family tree. Also, in existing methods, verification and integration of information are performed manually, which is time-consuming and laborious, and may result in low reliability.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides means for automatically collecting data on a network based on key information, and means for performing image analysis and natural language processing on the collected data to extract relevant information. Furthermore, it includes means for determining the reliability of the extracted information and generating a family tree by integrating the highly reliable data. This makes it possible to easily and automatically generate a highly accurate family tree and provide users with relevant information and suggestions based on the generated family tree.

[0006] "Key information" refers to basic personal information that users enter for their own family history research, specifically including name, place of origin, and age.

[0007] "Automatically collecting data" refers to the process by which a system independently searches for and retrieves relevant digital data from various information sources on the internet, based on key information provided by the user.

[0008] "Image analysis" is a technology that uses AI algorithms to analyze image information contained in collected digital data and extract visual features and content.

[0009] "Natural language processing" is a technology that analyzes collected text data to understand its grammar and meaning, with the aim of extracting specific patterns and information.

[0010] "Assessing reliability" refers to the process of evaluating the accuracy and reliability of the source of obtained information, thereby eliminating uncertain information and utilizing only reliable information.

[0011] "Integrating information" refers to the process of identifying interrelationships based on analyzed information and combining them into a single dataset.

[0012] "Generating a family tree" is the process of visualizing and representing, as a diagram, the relationships between individual people and family members based on integrated information.

[0013] "Suggesting relevant information" refers to recommending historical data, related services, and products that may be useful to the user based on the generated family tree. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a data aggregation system designed to enable individuals to automatically generate highly accurate family trees. Based on key information entered by the user through an interface, this system efficiently collects digital data scattered across a network and performs necessary analysis to accurately illustrate family relationships.

[0036] First, the user enters the necessary key information into the system via a terminal. This includes their name, place of origin, and relevant time period. Based on this information, the server automatically searches web databases and collects relevant data from various digital information sources (e.g., old photographs, historical documents, local information, etc.).

[0037] The server applies image analysis techniques to the collected data to extract information about people and families contained in photographs and documents. It also uses natural language processing to analyze specific relationships and historical information from the collected text data. The results of this analysis are evaluated based on their reliability and integrated into a highly reliable dataset within the system.

[0038] This process allows the server to execute an algorithm that generates a family tree from the integrated information. The generated family tree visually shows the relationships between each individual and is displayed on the user's terminal. Furthermore, based on the generated family tree, the server suggests relevant historical information and recommends meaningful tour plans and products to the user.

[0039] For example, if a user attempts to generate a family tree based on information about their great-grandfather, the server can collect relevant religious census records and gravestone information from the great-grandfather's name and place of origin, check its reliability and consistency, and then provide the user with a family history from the great-grandfather to the present. This system allows users to gain a detailed understanding of their roots, discover new family members, and gain insights into historical background.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user accesses the system interface through a terminal and enters key information for generating the family tree. This includes the names of specific individuals, their places of origin, and relevant time periods.

[0043] Step 2:

[0044] The server automatically searches numerous digital databases on the internet based on the key information entered by the user and collects relevant digital data. This process utilizes search engine APIs and web crawler technologies to extract old photographs, historical documents, and other relevant information.

[0045] Step 3:

[0046] The server applies image analysis algorithms to the collected digital data. This allows it to identify people and objects in photographs and diagrams and perform optical character recognition (OCR) to extract textual information.

[0047] Step 4:

[0048] The server analyzes the collected text data using natural language processing (NLP) techniques. In this process, it identifies keywords that indicate specific family history or historical relationships and understands their meaning.

[0049] Step 5:

[0050] The server calculates a reliability score for the acquired data and determines the reliability of the information based on evaluation criteria. Irrelevant or unreliable information is excluded, and reliable information is integrated to create a dataset.

[0051] Step 6:

[0052] The server uses an AI algorithm to generate a family tree based on the integrated dataset. The family tree visually represents relationships and shows the direct and branch family structure.

[0053] Step 7:

[0054] The server sends the generated family tree to the user's terminal and displays it on the interface. The user can view it, enter additional information as needed, and check related historical information and sightseeing plans suggested by the system.

[0055] Step 8:

[0056] Users can provide feedback based on the options and suggestions offered by the system. Upon receiving the feedback, the server implements a learning process to further improve the accuracy of the data.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Traditional methods for generating genealogical information required considerable time and effort to manually collect information from each individual and analyze its relationships. Furthermore, finding reliable data was difficult, and the data was prone to containing misinformation. Additionally, the suggestions for related information based on the generated genealogies were incomplete, making it difficult for users to easily obtain useful knowledge.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for receiving information based on user input, means for automatically acquiring data from a wide-area information network based on key information, and means for performing visual data analysis and natural language analysis on the acquired data and extracting relevant information. As a result, users can quickly and accurately obtain their family history information, and reliable genealogical information can be generated and visualized. Furthermore, useful history-based information can be suggested to the user, improving the efficiency of information retrieval and expanding knowledge.

[0062] "User input" refers to information provided by the user through their device, and it forms the basic data for genealogy generation.

[0063] A "wide-area information network" refers to a place where information is collected, including the internet and digital communication networks, and is the target of data collection.

[0064] "Visual data analysis" is a technology that analyzes image and video data to extract useful information, and includes facial recognition and text recognition.

[0065] "Natural language processing" is a technology that uses algorithms to analyze text data and extract meaning and relationships from it, and includes text mining and content comprehension.

[0066] "Reliability" is a measure used to evaluate the accuracy and relevance of information, and is used in data integration.

[0067] A "genealogical chart" is a diagram that visually represents the relationships between people in a family or group, showing the connections between each individual.

[0068] "Relevant historical information" refers to historical and cultural information suggested based on the generated genealogical chart, intended to deepen the user's understanding.

[0069] This invention is a system designed to allow users to easily generate highly accurate genealogical charts. The system mainly consists of a user terminal and a server, each playing a specific role.

[0070] Users access the system via a terminal. A user interface is provided for entering key information such as personal name, place of origin, and relevant age group. This interface is intuitive, allowing users to easily input the necessary information.

[0071] The entered information is sent to the server. Based on this key information, the server searches databases on a wide-area information network. To achieve this, the server uses a web crawler built with programming languages ​​such as Python or Java (registered trademark). The crawler searches publicly available databases and digital archives on the internet and quickly retrieves relevant data.

[0072] The server performs visual data analysis and natural language processing on the acquired data. For visual data analysis, the open-source image processing library OpenCV is used. The server uses this library to identify individuals from images and photographs and extract relevant information. For natural language processing, the natural language processing toolkit NLTK is used. The server utilizes this to interpret and analyze genealogical information and historical relationships from the collected text data.

[0073] The analyzed information is integrated based on its reliability and formed into a visual genealogical chart using advanced algorithms. This chart is transmitted from the server to the terminal and displayed to the user. In addition, the server has a function to suggest relevant historical information and tourist destinations to the user based on the generated genealogical chart.

[0074] For example, if a user inputs information about their grandfather's name and birthplace into the system, the server will collect and analyze as much detailed family history information as possible based on that information. If a user enters a prompt such as, "Generate a family tree based on my grandfather's name and suggest relevant historical information," the AI ​​can generate appropriate output.

[0075] In this way, one can gain a deep understanding of their own family lineage and its historical background.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user accesses the system using a terminal and enters key information necessary for generating the family tree. Specifically, they enter their name, place of origin, relevant historical period, etc., into the interface. The entered information is sent directly to the server and stored as initial data. This input information serves as the basis for subsequent data collection and analysis.

[0079] Step 2:

[0080] The server searches databases across a wide-area information network based on key information received from the user. The server efficiently retrieves relevant digital information using a web crawler. This crawler collects data from open data, library archives, and online resources containing historical information. A data search is performed based on the input key information, and relevant data is stored on the server as output.

[0081] Step 3:

[0082] The server performs visual data analysis on the collected data. Using open-source image analysis libraries, it extracts information such as individual faces and names from photographs and images. This step analyzes the visual information obtained from the images and generates output data associated with specific individuals. Specifically, it uses facial recognition technology to identify each person in the photograph.

[0083] Step 4:

[0084] Next, the server performs natural language processing. It analyzes the collected text data using natural language processing tools to identify family relationships and historical context. In this step, language analysis is performed based on the input text data. The output includes information correlations and important historical information. Specifically, it performs text mining to understand family history from the context.

[0085] Step 5:

[0086] The server integrates the information extracted through analysis and evaluates its reliability. It selects highly reliable information and integrates it as a dataset for genealogical tree generation. This integrated data is then used directly as input to the genealogical tree generation algorithm. This reliability evaluation eliminates erroneous information.

[0087] Step 6:

[0088] The genealogy generation algorithm generates a family tree using integrated data. The server executes the algorithm and constructs a genealogy diagram that visually shows each individual and their relationships. The output is generated as a genealogy diagram within the server and finally sent to the terminal. Specifically, it provides a graphical representation using nodes and lines to show individuals and their relationships.

[0089] Step 7:

[0090] The terminal displays the generated genealogy chart to the user. Furthermore, the server suggests historical context and interesting regional information based on the genealogy chart. This allows the user to gain a deeper understanding of their own historical background. The output of this step includes the genealogy chart displayed on the terminal and the suggested information.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Existing genealogy systems only display an individual's historical information, making it difficult to provide a personalized and meaningful customer experience using that information. Furthermore, the inability to recommend relevant products based on highly reliable data prevents maximizing user value. This invention aims to solve this problem by utilizing an individual's genealogical information to suggest products related to their historical background, thereby providing a deeper, more personalized experience.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for automatically collecting data on the network based on key information, means for integrating the collected data and generating a family tree, and means for suggesting relevant products to the user in a virtual store using the generated family tree and associated historical information. This makes it possible for the user to receive product suggestions that utilize the historical background based on their own family lineage.

[0096] "Key information" refers to information provided by users, such as their personal name and place of origin, which serves as the basis for collecting relevant data from the network.

[0097] "Data on the network" refers to a collection of various digital information accessible via the internet, and is the source of the information necessary to generate a family tree.

[0098] "Image analysis" is a technology that uses computers to visually analyze information contained in photographs and documents, and extract information related to specific individuals or families.

[0099] Natural language processing is a technology that enables computers to understand and analyze human language, and is used to identify relationships and historical information from collected text data.

[0100] "Reliability" is an indicator that shows the accuracy and consistency of the collected information, and it is used to judge the value of the information when it is integrated.

[0101] A "family tree" is a diagram that visually shows the blood relationships and family structure between individuals, and is generated based on collected data.

[0102] A "virtual store" is a virtual shop built on the internet, a platform for users to explore and purchase goods online.

[0103] "Related products" are products suggested to users based on their personal family history and background, and their selection takes into account historical and familial context.

[0104] The system for implementing this invention consists of a program that collects and analyzes digital data. Here, we describe a specific form of a virtual store that automatically collects data from a network based on personal information, analyzes it, and then creates a personalized family tree and provides product suggestions based on it.

[0105] The server operates in a high-performance computing environment and collects publicly accessible network data from the internet using "key information" provided by the user. During this process, it utilizes the OpenCV library to perform "image analysis" and extract information related to people and families from image data. Furthermore, it uses the Python NLTK library for "natural language processing" to identify human relationships and historical information from text data. This allows for the evaluation of the "reliability" of the collected information, enabling advanced data integration.

[0106] Based on this integrated information, a "family tree" is constructed, allowing users to trace their roots through it. Furthermore, the "virtual store" displays "related products" associated with this family tree. This allows users to explore products based on their family's historical background and enjoy a personalized shopping experience.

[0107] As a concrete example, a user can input information about their great-grandfather to generate a family tree, and traditional crafts and local specialties related to his birthplace will be showcased in a virtual store. Users can view these items using smart glasses or purchase related products via their smartphone.

[0108] An example of a user entering a prompt using the interface is, "Please view my great-grandfather's family tree and display products related to that historical background." Based on this prompt, the system automatically collects and outputs the relevant information.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users input "key information," such as their name and place of origin, into the interface via their terminal. This input information becomes the system's basic data.

[0112] Step 2:

[0113] Based on the key information received from the user, the server accesses publicly available databases on the network and automatically collects relevant "data on the network." At this stage, search queries are generated from the database, and the corresponding digital data is aggregated on the server.

[0114] Step 3:

[0115] The server uses the OpenCV library to perform "image analysis" on the collected image data. The input is the collected image data, and by performing face recognition and object detection, it outputs information related to people and families.

[0116] Step 4:

[0117] The server uses the Python NLTK library to perform "natural language processing" on the collected text data. It analyzes relationships and historical information from the input text and extracts specific keywords and phrases. As a result, text information explaining family relationships is output.

[0118] Step 5:

[0119] The server uses the extracted information to evaluate its "reliability." This reliability evaluation takes into account the reliability of the information source and the consistency of the data, and the most reliable information is selected.

[0120] Step 6:

[0121] The server integrates reliable information and generates a "family tree." This family tree is represented by nodes and edges, visually showing the relationships between individual family members. The output is sent to the terminal as a family tree.

[0122] Step 7:

[0123] The user displays a family tree generated on their device and searches for related products through a virtual store. The server selects "related products" based on the family tree and displays them in the virtual store's interface.

[0124] Step 8:

[0125] Users experience related products in a virtual store through smart glasses or smartphones. Based on their interest in these products and their willingness to purchase, users provide feedback, and the system optimizes its recommendation algorithm based on this feedback.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention is a system that recognizes the user's emotions and, based on those emotions, generates an optimal family tree and suggests related information. The system is designed to provide information optimized for the user by performing data analysis that combines personal information entered by the user with an emotion engine.

[0128] First, the user accesses the system via a terminal and enters key information necessary to create the family tree. This information includes basic personal details such as name, place of origin, and age. The server uses this information to automatically collect relevant digital data from the network.

[0129] The collected data is analyzed by a server using image analysis and natural language processing technologies. This process extracts relevant information from photographs and documents. Furthermore, reliability algorithms are used during this analysis process, and only reliable information is integrated.

[0130] Next, the server uses an emotion engine to analyze the user's input and responses as they use the system and identify their emotions. This emotion information is taken into consideration in the family tree generation process and in suggesting additional information, providing content that matches the user's emotions. In this way, the system provides the most relevant family tree information and related suggestions to the user, improving the user experience. For example, if the user expresses excitement or joy, the server deepens the user's interest by suggesting further details and activities related to these feelings.

[0131] The introduction of this system allows users to better understand their family history and experience greater emotional satisfaction. The system's emotion recognition capabilities play a crucial role in providing users with real-time information and feedback that meets their expectations.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user accesses the system interface via a terminal and enters the key information necessary to begin generating the family tree. This information includes name, place of origin, and age. This prepares the system to efficiently search for information related to the user's family history.

[0135] Step 2:

[0136] The server searches various data sources on the network based on key information provided by the user and collects relevant digital data. This data includes old photographs, historical documents, and historical records. Data collection is performed automatically by pre-configured search engine APIs and web crawlers.

[0137] Step 3:

[0138] The server performs image analysis on the collected digital data. This process extracts information from photographs and documents using algorithms to identify people and objects. Simultaneously, it applies OCR technology to convert text information within images into text data.

[0139] Step 4:

[0140] The server analyzes the text data obtained in the previous step using natural language processing techniques. Here, it extracts information related to family lineage from sentences and phrases, identifying important keywords and relationships. At this stage, relationships between individuals and historical connections are identified.

[0141] Step 5:

[0142] The server uses an algorithm to determine the reliability of the extracted information and calculates a reliability score for each data point. Uncertain data is eliminated, and only highly reliable information is selected and integrated. This process ensures that users can use accurate information with confidence.

[0143] Step 6:

[0144] The server utilizes an emotion engine to analyze user input and responses, identifying the user's emotional state (joy, surprise, interest, etc.). This emotional information influences the display of generated family trees and suggested additional information, and is used to personalize the user experience.

[0145] Step 7:

[0146] The server integrates reliable information and generates a family tree, taking into account emotional data obtained from the emotion engine. The family tree visually shows relationships and is displayed on the device in a format that is easy for the user to understand.

[0147] Step 8:

[0148] Users view the displayed family tree and accompanying information on their device and select additional information suggested by the system based on their interests and feelings. Users can provide feedback, and the system continuously improves its accuracy and user experience based on this feedback.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In modern society, many people are interested in their family history and roots, but researching them is difficult for individuals due to the complexity of information gathering and organization. Furthermore, it is necessary not only to provide information but also to analyze individual emotions and provide relevant information to improve the user experience. This invention aims to solve this problem by providing a system that efficiently and reliably collects information and enables the generation of a family tree tailored to the user, along with related suggestions.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for automatically collecting information on a communication network based on key information; means for performing image processing and language processing on the collected information and extracting relevant data; means for evaluating the reliability of the extracted data and integrating the highly reliable data; means for constructing a genealogical chart using the integrated data; means for providing additional relevant data based on the constructed genealogical chart; and means for identifying emotions through user input and optimizing additional information based on those emotions. This makes it possible to provide highly reliable and relevant family information while taking into account individual emotions.

[0154] "Key information" refers to basic personal information entered by the user, such as name, place of origin, and age, and serves as the basis for automatically collecting data.

[0155] A "communication network" refers to the entire internet and data network used for sending and receiving information, and is the infrastructure used for acquiring and processing data.

[0156] "Image processing" is a technique that analyzes collected image data and extracts useful information, and includes techniques such as recognizing specific patterns and faces.

[0157] "Language processing" refers to techniques for analyzing text data and extracting meaningful information, and includes natural language processing.

[0158] "Reliability" is an indicator that shows the accuracy and credibility of collected information, and is evaluated based on the source of the information and the results of verification.

[0159] A "genealogical chart" is a diagram that visually represents the family history of a particular family or individual, showing family relationships and generations.

[0160] "Emotions" refer to the feelings and reactions that users exhibit when using a system, and are psychological states analyzed from input data and behavior.

[0161] To implement this invention, the user first accesses the system via a terminal and inputs key information necessary for generating a family tree. This key information includes basic personal information such as name, place of origin, and age. This information serves as the basis for data collection.

[0162] The server automatically collects relevant digital data using the communication network based on the entered key information. The technologies used include web scraping tools to crawl the internet and APIs for data acquisition. Generative AI models are also utilized for natural language processing. For example, the server searches for and retrieves relevant information from genealogical records and photo databases.

[0163] The collected data is analyzed by the server using image processing and natural language processing technologies. For example, facial recognition algorithms may be used for image processing. This allows for the identification of individuals from photographs and the extraction of relevant information. In addition, natural language processing extracts personal names and date information from text. This process utilizes generative AI models to achieve highly accurate information extraction.

[0164] Furthermore, the server evaluates the reliability of the data, selecting and integrating only the most reliable information. The reliability assessment considers factors such as the source of the information and its consistency with other information. For example, information from official records is considered highly reliable.

[0165] When a user uses the system, the server analyzes the user's input and actions using an emotion engine to identify the user's emotions. This emotion information is then used to generate the optimal family tree and suggest relevant information. Specifically, when a user expresses enjoyment, information about episodes and related events that match that emotion is presented.

[0166] Finally, an example of a prompt is given: "Build an algorithm that provides highly relevant genealogical information and suggestions based on the user's input and emotions. The user has entered their grandfather's name and place of origin. Optimize the analysis of relevant historical documents and photographs based on reliability, and suggest detailed information about local history if emotions are confirmed." This prompt serves as the foundation for improving the user experience using a generative AI model.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] Users access the system via a terminal and enter key information such as their name, place of origin, and age. This input serves as the basis for data collection.

[0170] Step 2:

[0171] The server automatically collects relevant digital data using the communication network based on the key information entered by the user. Here, web scraping tools and APIs are used to explore online genealogical records and photo databases. The input is the user's key information, and the output is the collected dataset.

[0172] Step 3:

[0173] The server performs image processing and language processing on the collected data and extracts relevant data. Specifically, it uses facial recognition technology for image processing and a generative AI model for language processing. The input to this process is the collected data from the previous step, and the output is the extracted target information.

[0174] Step 4:

[0175] The server evaluates the reliability of the extracted data. To evaluate reliability, the source of the data is verified and consistency is checked. The input here is the extracted information, and the output is reliable data.

[0176] Step 5:

[0177] The server integrates reliable data and generates a genealogical chart. Data integration organizes relevant information based on chronological order and relationships, creating a visualized genealogical chart. The input to this process is reliable extracted data, and the output is the generated genealogical chart.

[0178] Step 6:

[0179] The server identifies the user's emotions using an emotion engine. It analyzes the user's interaction data and input. The input for this step is the user's response data, and the output is the identified emotion information.

[0180] Step 7:

[0181] The server provides additional information and suggestions tailored to the user's emotions based on the genealogy diagram. Emotion-based information optimization includes presenting relevant events and episodes. The input for this step is the generated genealogy diagram and emotion information, and the output is optimized suggestion information.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] In today's information environment, users have access to a vast amount of information, but finding it useful and interesting is difficult. In particular, information related to family history and genealogy can lead to misunderstandings if it lacks reliability or relevance. Furthermore, methods for providing personalized information based on user emotions are limited. There is a need to solve these problems and enable users to easily find relevant information and develop an interest in it.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for automatically collecting data on the network based on key information, means for performing image analysis and natural language processing on the collected data to extract relevant information, and means for determining the reliability of the extracted information and integrating the reliable information. This enables users to efficiently acquire information that is highly relevant and reliable to them, thereby improving their psychological satisfaction.

[0187] "Key information" refers to personal information entered by users, such as their name, place of origin, and age, and forms the basis for data collection and analysis.

[0188] "Data on the network" refers to a collection of digital information accessible through the internet or cloud services, and serves as a source of information for generating family trees.

[0189] "Image analysis" refers to the technique of extracting useful information from collected digital images, and is a method that improves the reliability of information when combined with natural language processing.

[0190] "Natural language processing" refers to techniques for understanding meaning from collected document data, and is used in combination with image analysis to extract relevant information.

[0191] "Related information" refers to data useful for generating family trees and suggesting information to users, and is reliable and integrated information.

[0192] "Identifying emotions" refers to the process of identifying a user's psychological state at a given moment based on their input and responses.

[0193] "Visual devices" refer to display devices such as smart glasses, which are devices used to display information to the user.

[0194] The embodiments for carrying out the invention are described below.

[0195] To implement this invention, the user must wear a dedicated visual terminal, such as smart glasses. The user inputs key information via the visual terminal. This key information includes the user's personal information, such as name, place of origin, and age. The server then automatically collects data from the network.

[0196] The server attempts to analyze the collected data using image analysis techniques (e.g., libraries such as OpenCV) and natural language processing techniques (such as NLTK). This analysis involves extracting information from digital images and understanding the meaning of document data, thereby extracting highly reliable relevant information. Reliability is determined by calculating a reliability score for the data.

[0197] Furthermore, the server uses emotion recognition technology to identify the user's emotions when using the visual device. This emotion data influences the generation of the family tree, and relevant information is also adjusted according to the user's emotions. Finally, the necessary information is displayed on the smart glasses and provided visually based on the user's emotions.

[0198] For example, when a user expresses interest, relevant family history information and past events are displayed according to that emotion, amplifying the user's interest.

[0199] An example of a prompt to input into a generative AI model would be: "Identify emotions from this user's facial expression data and provide relevant family history information in real time. The user's name is a common name, and they are interested in past history."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The server receives key information entered by the user. Based on the entered information such as name, place of origin, and age, it accesses publicly available databases and cloud resources on the internet and automatically collects relevant data. The output at this stage is a collection of digital data related to the individual user.

[0203] Step 2:

[0204] The server performs image analysis and natural language processing on the collected data. It recognizes people and specific symbols from image data and extracts relevant text information from document data. It uses OpenCV and NLTK to evaluate the reliability of the data and filter out relevant information. The output of this step is a reliable set of information relevant to the user.

[0205] Step 3:

[0206] The server generates a family tree using the obtained relevant information. Here, the extracted information is incorporated into a tree-like data structure and transformed into a visually easy-to-understand format. The generated family tree is the output.

[0207] Step 4:

[0208] The device uses real-time facial expression data collected from the user's visual device to identify the user's emotions. Using facial recognition technology, an emotion engine analyzes what emotions the user is expressing. The output is the identified emotion.

[0209] Step 5:

[0210] The server adjusts the generated family tree and related information based on the identified emotions. It selects relevant information that matches the emotion expressed by the user and generates prompts to display on the smart glasses. The output of this step is information presented according to the emotion.

[0211] Step 6:

[0212] The terminal displays and provides the user with adjusted information on a visual terminal. The display screen shows relevant information and family history. This allows the user to intuitively understand their own history and interesting information. The output is a visual display of information provided to the user.

[0213] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0214] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0220] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0224] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0226] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0229] This invention is a data aggregation system designed to enable individuals to automatically generate highly accurate family trees. Based on key information entered by the user through an interface, this system efficiently collects digital data scattered across a network and performs necessary analysis to accurately illustrate family relationships.

[0230] First, the user enters the necessary key information into the system via a terminal. This includes their name, place of origin, and relevant time period. Based on this information, the server automatically searches web databases and collects relevant data from various digital information sources (e.g., old photographs, historical documents, local information, etc.).

[0231] The server applies image analysis techniques to the collected data to extract information about people and families contained in photographs and documents. It also uses natural language processing to analyze specific relationships and historical information from the collected text data. The results of this analysis are evaluated based on their reliability and integrated into a highly reliable dataset within the system.

[0232] This process allows the server to execute an algorithm that generates a family tree from the integrated information. The generated family tree visually shows the relationships between each individual and is displayed on the user's terminal. Furthermore, based on the generated family tree, the server suggests relevant historical information and recommends meaningful tour plans and products to the user.

[0233] For example, if a user attempts to generate a family tree based on information about their great-grandfather, the server can collect relevant religious census records and gravestone information from the great-grandfather's name and place of origin, check its reliability and consistency, and then provide the user with a family history from the great-grandfather to the present. This system allows users to gain a detailed understanding of their roots, discover new family members, and gain insights into historical background.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user accesses the system interface through a terminal and enters key information for generating the family tree. This includes the names of specific individuals, their places of origin, and relevant time periods.

[0237] Step 2:

[0238] The server automatically searches numerous digital databases on the internet based on the key information entered by the user and collects relevant digital data. This process utilizes search engine APIs and web crawler technologies to extract old photographs, historical documents, and other relevant information.

[0239] Step 3:

[0240] The server applies image analysis algorithms to the collected digital data. This allows it to identify people and objects in photographs and diagrams and perform optical character recognition (OCR) to extract textual information.

[0241] Step 4:

[0242] The server analyzes the collected text data using natural language processing (NLP) techniques. In this process, it identifies keywords that indicate specific family history or historical relationships and understands their meaning.

[0243] Step 5:

[0244] The server calculates a reliability score for the acquired data and determines the reliability of the information based on evaluation criteria. Irrelevant or unreliable information is excluded, and reliable information is integrated to create a dataset.

[0245] Step 6:

[0246] The server uses an AI algorithm to generate a family tree based on the integrated dataset. The family tree visually represents relationships and shows the direct and branch family structure.

[0247] Step 7:

[0248] The server sends the generated family tree to the user's terminal and displays it on the interface. The user can view it, enter additional information as needed, and check related historical information and sightseeing plans suggested by the system.

[0249] Step 8:

[0250] Users can provide feedback based on the options and suggestions offered by the system. Upon receiving the feedback, the server implements a learning process to further improve the accuracy of the data.

[0251] (Example 1)

[0252] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0253] Traditional methods for generating genealogical information required considerable time and effort to manually collect information from each individual and analyze its relationships. Furthermore, finding reliable data was difficult, and the data was prone to containing misinformation. Additionally, the suggestions for related information based on the generated genealogies were incomplete, making it difficult for users to easily obtain useful knowledge.

[0254] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0255] In this invention, the server includes means for receiving information based on user input, means for automatically acquiring data from a wide-area information network based on key information, and means for performing visual data analysis and natural language analysis on the acquired data and extracting relevant information. As a result, users can quickly and accurately obtain their family history information, and reliable genealogical information can be generated and visualized. Furthermore, useful history-based information can be suggested to the user, improving the efficiency of information retrieval and expanding knowledge.

[0256] "User input" refers to information provided by the user through their device, and it forms the basic data for genealogy generation.

[0257] A "wide-area information network" refers to a place where information is collected, including the internet and digital communication networks, and is the target of data collection.

[0258] "Visual data analysis" is a technology that analyzes image and video data to extract useful information, and includes facial recognition and text recognition.

[0259] "Natural language processing" is a technology that uses algorithms to analyze text data and extract meaning and relationships from it, and includes text mining and content comprehension.

[0260] "Reliability" is a measure used to evaluate the accuracy and relevance of information, and is used in data integration.

[0261] A "genealogical chart" is a diagram that visually represents the relationships between people in a family or group, showing the connections between each individual.

[0262] "Relevant historical information" refers to historical and cultural information suggested based on the generated genealogical chart, intended to deepen the user's understanding.

[0263] This invention is a system designed to allow users to easily generate highly accurate genealogical charts. The system mainly consists of a user terminal and a server, each playing a specific role.

[0264] Users access the system via a terminal. A user interface is provided for entering key information such as personal name, place of origin, and relevant age group. This interface is intuitive, allowing users to easily input the necessary information.

[0265] The entered information is sent to the server. Based on this key information, the server searches databases on a wide-area information network. To achieve this, the server uses a web crawler built with programming languages ​​such as Python or Java. The crawler searches publicly available databases and digital archives on the internet and quickly retrieves relevant data.

[0266] The server performs visual data analysis and natural language processing on the acquired data. For visual data analysis, the open-source image processing library OpenCV is used. The server uses this library to identify individuals from images and photographs and extract relevant information. For natural language processing, the natural language processing toolkit NLTK is used. The server utilizes this to interpret and analyze genealogical information and historical relationships from the collected text data.

[0267] The analyzed information is integrated based on its reliability and formed into a visual genealogical chart using advanced algorithms. This chart is transmitted from the server to the terminal and displayed to the user. In addition, the server has a function to suggest relevant historical information and tourist destinations to the user based on the generated genealogical chart.

[0268] For example, if a user inputs information about their grandfather's name and birthplace into the system, the server will collect and analyze as much detailed family history information as possible based on that information. If a user enters a prompt such as, "Generate a family tree based on my grandfather's name and suggest relevant historical information," the AI ​​can generate appropriate output.

[0269] In this way, one can gain a deep understanding of their own family lineage and its historical background.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The user accesses the system using a terminal and enters key information necessary for generating the family tree. Specifically, they enter their name, place of origin, relevant historical period, etc., into the interface. The entered information is sent directly to the server and stored as initial data. This input information serves as the basis for subsequent data collection and analysis.

[0273] Step 2:

[0274] The server searches databases across a wide-area information network based on key information received from the user. The server efficiently retrieves relevant digital information using a web crawler. This crawler collects data from open data, library archives, and online resources containing historical information. A data search is performed based on the input key information, and relevant data is stored on the server as output.

[0275] Step 3:

[0276] The server performs visual data analysis on the collected data. Using open-source image analysis libraries, it extracts information such as individual faces and names from photographs and images. This step analyzes the visual information obtained from the images and generates output data associated with specific individuals. Specifically, it uses facial recognition technology to identify each person in the photograph.

[0277] Step 4:

[0278] Next, the server performs natural language processing. It analyzes the collected text data using natural language processing tools to identify family relationships and historical context. In this step, language analysis is performed based on the input text data. The output includes information correlations and important historical information. Specifically, it performs text mining to understand family history from the context.

[0279] Step 5:

[0280] The server integrates the information extracted by the analysis and evaluates its reliability. It selects highly reliable information and integrates it as a dataset for generating the family tree. The integrated data is directly used as the input for the family tree generation algorithm. This reliability evaluation eliminates incorrect information.

[0281] Step 6:

[0282] The family tree generation algorithm uses the integrated data to generate a family tree. The server executes the algorithm and constructs a family tree that visually shows each individual and their relationships. The output is generated as a family tree within the server and ultimately sent to the terminal. Specifically, a graphical display is made using nodes and lines to show individuals and their relationships.

[0283] Step 7:

[0284] The terminal displays the generated family tree to the user. Furthermore, based on the family tree, the server proposes historical background and interesting regional information. This enables the user to better understand their own historical background. The output of this step includes the family tree displayed on the terminal and the proposed information.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] Existing family tree creation systems only display the historical information of individuals, and there is a problem that it is difficult to provide an individualized and meaningful customer experience using this information. Also, since it is difficult to make relevant recommendations for products based on highly reliable data, the value for the user has not been maximized. This invention aims to solve this problem by leveraging an individual's family information to propose products related to the historical background to the user and provide a more deeply personalized experience. <s

[0288] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for automatically collecting data on the network based on key information, means for integrating the collected data and generating a family tree, and means for suggesting relevant products to the user in a virtual store using the generated family tree and associated historical information. This makes it possible for the user to receive product suggestions that utilize the historical background based on their own family lineage.

[0290] "Key information" refers to information provided by users, such as their personal name and place of origin, which serves as the basis for collecting relevant data from the network.

[0291] "Data on the network" refers to a collection of various digital information accessible via the internet, and is the source of the information necessary to generate a family tree.

[0292] "Image analysis" is a technology that uses computers to visually analyze information contained in photographs and documents, and extract information related to specific individuals or families.

[0293] Natural language processing is a technology that enables computers to understand and analyze human language, and is used to identify relationships and historical information from collected text data.

[0294] "Reliability" is an indicator that shows the accuracy and consistency of the collected information, and it is used to judge the value of the information when it is integrated.

[0295] A "family tree" is a diagram that visually shows the blood relationships and family structure between individuals, and is generated based on collected data.

[0296] A "virtual store" is a virtual shop built on the internet, a platform for users to explore and purchase goods online.

[0297] "Related products" are products suggested to users based on their personal family history and background, and their selection takes into account historical and familial context.

[0298] The system for implementing this invention consists of a program that collects and analyzes digital data. Here, we describe a specific form of a virtual store that automatically collects data from a network based on personal information, analyzes it, and then creates a personalized family tree and provides product suggestions based on it.

[0299] The server operates in a high-performance computing environment and collects publicly accessible network data from the internet using "key information" provided by the user. During this process, it utilizes the OpenCV library to perform "image analysis" and extract information related to people and families from image data. Furthermore, it uses the Python NLTK library for "natural language processing" to identify human relationships and historical information from text data. This allows for the evaluation of the "reliability" of the collected information, enabling advanced data integration.

[0300] Based on this integrated information, a "family tree" is constructed, allowing users to trace their roots through it. Furthermore, the "virtual store" displays "related products" associated with this family tree. This allows users to explore products based on their family's historical background and enjoy a personalized shopping experience.

[0301] As a concrete example, a user can input information about their great-grandfather to generate a family tree, and traditional crafts and local specialties related to his birthplace will be showcased in a virtual store. Users can view these items using smart glasses or purchase related products via their smartphone.

[0302] As an example of a user inputting a prompt sentence using the interface, "Please view the family tree of my great-grandfather and display products related to its historical background" can be cited. Based on this prompt sentence, the system automatically collects and outputs the corresponding information.

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The user inputs "key information" such as personal name and place of birth into the interface through the terminal. This input information becomes the basic data of the system.

[0306] Step 2:

[0307] Based on the key information received from the user, the server accesses the public database on the network and automatically collects the related "data on the network". At this stage, a search query from the database is generated, and the corresponding digital data is aggregated on the server.

[0308] Step 3:

[0309] The server uses the OpenCV library to perform "image analysis" on the collected image data. The input is the collected image data, and by performing face recognition and object detection, information related to people and families is output.

[0310] Step 4:

[0311] The server uses the NLTK library of Python to perform "natural language processing" on the collected text data. It analyzes the relationships and historical information from the input text and extracts specific keywords and phrases. As a result, text information explaining family relationships is output.

[0312] Step 5:

[0313] The server uses the extracted information to evaluate its "reliability." This reliability evaluation takes into account the reliability of the information source and the consistency of the data, and the most reliable information is selected.

[0314] Step 6:

[0315] The server integrates reliable information and generates a "family tree." This family tree is represented by nodes and edges, visually showing the relationships between individual family members. The output is sent to the terminal as a family tree.

[0316] Step 7:

[0317] The user displays a family tree generated on their device and searches for related products through a virtual store. The server selects "related products" based on the family tree and displays them in the virtual store's interface.

[0318] Step 8:

[0319] Users experience related products in a virtual store through smart glasses or smartphones. Based on their interest in these products and their willingness to purchase, users provide feedback, and the system optimizes its recommendation algorithm based on this feedback.

[0320] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0321] This invention is a system that recognizes the user's emotions and, based on those emotions, generates an optimal family tree and suggests related information. The system is designed to provide information optimized for the user by performing data analysis that combines personal information entered by the user with an emotion engine.

[0322] First, the user accesses the system via a terminal and enters key information necessary to create the family tree. This information includes basic personal details such as name, place of origin, and age. The server uses this information to automatically collect relevant digital data from the network.

[0323] The collected data is analyzed by a server using image analysis and natural language processing technologies. This process extracts relevant information from photographs and documents. Furthermore, reliability algorithms are used during this analysis process, and only reliable information is integrated.

[0324] Next, the server uses an emotion engine to analyze the user's input and responses as they use the system and identify their emotions. This emotion information is taken into consideration in the family tree generation process and in suggesting additional information, providing content that matches the user's emotions. In this way, the system provides the most relevant family tree information and related suggestions to the user, improving the user experience. For example, if the user expresses excitement or joy, the server deepens the user's interest by suggesting further details and activities related to these feelings.

[0325] The introduction of this system allows users to better understand their family history and experience greater emotional satisfaction. The system's emotion recognition capabilities play a crucial role in providing users with real-time information and feedback that meets their expectations.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user accesses the system interface via a terminal and enters the key information necessary to begin generating the family tree. This information includes name, place of origin, and age. This prepares the system to efficiently search for information related to the user's family history.

[0329] Step 2:

[0330] The server searches various data sources on the network based on key information provided by the user and collects relevant digital data. This data includes old photographs, historical documents, and historical records. Data collection is performed automatically by pre-configured search engine APIs and web crawlers.

[0331] Step 3:

[0332] The server performs image analysis on the collected digital data. This process extracts information from photographs and documents using algorithms to identify people and objects. Simultaneously, it applies OCR technology to convert text information within images into text data.

[0333] Step 4:

[0334] The server analyzes the text data obtained in the previous step using natural language processing techniques. Here, it extracts information related to family lineage from sentences and phrases, identifying important keywords and relationships. At this stage, relationships between individuals and historical connections are identified.

[0335] Step 5:

[0336] The server uses an algorithm to determine the reliability of the extracted information and calculates a reliability score for each data point. Uncertain data is eliminated, and only highly reliable information is selected and integrated. This process ensures that users can use accurate information with confidence.

[0337] Step 6:

[0338] The server utilizes an emotion engine to analyze user input and responses, identifying the user's emotional state (joy, surprise, interest, etc.). This emotional information influences the display of generated family trees and suggested additional information, and is used to personalize the user experience.

[0339] Step 7:

[0340] The server integrates reliable information and generates a family tree, taking into account emotional data obtained from the emotion engine. The family tree visually shows relationships and is displayed on the device in a format that is easy for the user to understand.

[0341] Step 8:

[0342] Users view the displayed family tree and accompanying information on their device and select additional information suggested by the system based on their interests and feelings. Users can provide feedback, and the system continuously improves its accuracy and user experience based on this feedback.

[0343] (Example 2)

[0344] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0345] In modern society, many people are interested in their family history and roots, but researching them is difficult for individuals due to the complexity of information gathering and organization. Furthermore, it is necessary not only to provide information but also to analyze individual emotions and provide relevant information to improve the user experience. This invention aims to solve this problem by providing a system that efficiently and reliably collects information and enables the generation of a family tree tailored to the user, along with related suggestions.

[0346] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0347] In this invention, the server includes means for automatically collecting information on a communication network based on key information; means for performing image processing and language processing on the collected information and extracting relevant data; means for evaluating the reliability of the extracted data and integrating the highly reliable data; means for constructing a genealogical chart using the integrated data; means for providing additional relevant data based on the constructed genealogical chart; and means for identifying emotions through user input and optimizing additional information based on those emotions. This makes it possible to provide highly reliable and relevant family information while taking into account individual emotions.

[0348] "Key information" refers to basic personal information entered by the user, such as name, place of origin, and age, and serves as the basis for automatically collecting data.

[0349] A "communication network" refers to the entire internet and data network used for sending and receiving information, and is the infrastructure used for acquiring and processing data.

[0350] "Image processing" is a technique that analyzes collected image data and extracts useful information, and includes techniques such as recognizing specific patterns and faces.

[0351] "Language processing" refers to techniques for analyzing text data and extracting meaningful information, and includes natural language processing.

[0352] "Reliability" is an indicator that shows the accuracy and credibility of collected information, and is evaluated based on the source of the information and the results of verification.

[0353] A "genealogical chart" is a diagram that visually represents the family history of a particular family or individual, showing family relationships and generations.

[0354] "Emotions" refer to the feelings and reactions that users exhibit when using a system, and are psychological states analyzed from input data and behavior.

[0355] To implement this invention, the user first accesses the system via a terminal and inputs key information necessary for generating a family tree. This key information includes basic personal information such as name, place of origin, and age. This information serves as the basis for data collection.

[0356] The server automatically collects relevant digital data using the communication network based on the entered key information. The technologies used include web scraping tools to crawl the internet and APIs for data acquisition. Generative AI models are also utilized for natural language processing. For example, the server searches for and retrieves relevant information from genealogical records and photo databases.

[0357] The collected data is analyzed by the server using image processing and natural language processing technologies. For example, facial recognition algorithms may be used for image processing. This allows for the identification of individuals from photographs and the extraction of relevant information. In addition, natural language processing extracts personal names and date information from text. This process utilizes generative AI models to achieve highly accurate information extraction.

[0358] Furthermore, the server evaluates the reliability of the data, selecting and integrating only the most reliable information. The reliability assessment considers factors such as the source of the information and its consistency with other information. For example, information from official records is considered highly reliable.

[0359] When a user uses the system, the server analyzes the user's input and actions using an emotion engine to identify the user's emotions. This emotion information is then used to generate the optimal family tree and suggest relevant information. Specifically, when a user expresses enjoyment, information about episodes and related events that match that emotion is presented.

[0360] Finally, an example of a prompt is given: "Build an algorithm that provides highly relevant genealogical information and suggestions based on the user's input and emotions. The user has entered their grandfather's name and place of origin. Optimize the analysis of relevant historical documents and photographs based on reliability, and suggest detailed information about local history if emotions are confirmed." This prompt serves as the foundation for improving the user experience using a generative AI model.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] Users access the system via a terminal and enter key information such as their name, place of origin, and age. This input serves as the basis for data collection.

[0364] Step 2:

[0365] The server automatically collects relevant digital data using the communication network based on the key information entered by the user. Here, web scraping tools and APIs are used to explore online genealogical records and photo databases. The input is the user's key information, and the output is the collected dataset.

[0366] Step 3:

[0367] The server performs image processing and language processing on the collected data and extracts relevant data. Specifically, it uses facial recognition technology for image processing and a generative AI model for language processing. The input to this process is the collected data from the previous step, and the output is the extracted target information.

[0368] Step 4:

[0369] The server evaluates the reliability of the extracted data. To evaluate reliability, the source of the data is verified and consistency is checked. The input here is the extracted information, and the output is reliable data.

[0370] Step 5:

[0371] The server integrates reliable data and generates a genealogical chart. Data integration organizes relevant information based on chronological order and relationships, creating a visualized genealogical chart. The input to this process is reliable extracted data, and the output is the generated genealogical chart.

[0372] Step 6:

[0373] The server identifies the user's emotions using an emotion engine. It analyzes the user's interaction data and input. The input for this step is the user's response data, and the output is the identified emotion information.

[0374] Step 7:

[0375] The server provides additional information and suggestions tailored to the user's emotions based on the genealogy diagram. Emotion-based information optimization includes presenting relevant events and episodes. The input for this step is the generated genealogy diagram and emotion information, and the output is optimized suggestion information.

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0378] In today's information environment, users have access to a vast amount of information, but finding it useful and interesting is difficult. In particular, information related to family history and genealogy can lead to misunderstandings if it lacks reliability or relevance. Furthermore, methods for providing personalized information based on user emotions are limited. There is a need to solve these problems and enable users to easily find relevant information and develop an interest in it.

[0379] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0380] In this invention, the server includes means for automatically collecting data on the network based on key information, means for performing image analysis and natural language processing on the collected data to extract relevant information, and means for determining the reliability of the extracted information and integrating the reliable information. This enables users to efficiently acquire information that is highly relevant and reliable to them, thereby improving their psychological satisfaction.

[0381] "Key information" refers to personal information entered by users, such as their name, place of origin, and age, and forms the basis for data collection and analysis.

[0382] "Network data" refers to a collection of digital information accessible through the internet or cloud services, and serves as a source of information for generating family trees.

[0383] "Image analysis" refers to techniques for extracting useful information from collected digital images, and is a method that improves the reliability of information when combined with natural language processing.

[0384] "Natural language processing" refers to techniques for understanding meaning from collected document data, and is used in combination with image analysis to extract relevant information.

[0385] "Related information" refers to data useful for generating family trees and suggesting information to users, and is reliable and integrated information.

[0386] "Identifying emotions" refers to the process of identifying a user's psychological state at a given moment based on their input and responses.

[0387] "Visual devices" refer to display devices such as smart glasses, which are devices used to display information to the user.

[0388] The embodiments for carrying out the invention are described below.

[0389] To implement this invention, the user must wear a dedicated visual terminal, such as smart glasses. The user inputs key information via the visual terminal. This key information includes the user's personal information, such as name, place of origin, and age. The server then automatically collects data from the network.

[0390] The server attempts to analyze the collected data using image analysis techniques (e.g., libraries such as OpenCV) and natural language processing techniques (such as NLTK). This analysis involves extracting information from digital images and understanding the meaning of document data, thereby extracting highly reliable relevant information. Reliability is determined by calculating a reliability score for the data.

[0391] Furthermore, the server uses emotion recognition technology to identify the user's emotions when using the visual device. This emotion data influences the generation of the family tree, and relevant information is also adjusted according to the user's emotions. Finally, the necessary information is displayed on the smart glasses and provided visually based on the user's emotions.

[0392] For example, when a user expresses interest, relevant family history information and past events are displayed according to that emotion, amplifying the user's interest.

[0393] An example of a prompt to input into a generative AI model would be: "Identify emotions from this user's facial expression data and provide relevant family history information in real time. The user's name is a common name, and they are interested in past history."

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The server receives key information entered by the user. Based on the entered information such as name, place of origin, and age, it accesses publicly available databases and cloud resources on the internet and automatically collects relevant data. The output at this stage is a collection of digital data related to the individual user.

[0397] Step 2:

[0398] The server performs image analysis and natural language processing on the collected data. It recognizes people and specific symbols from image data and extracts relevant text information from document data. It uses OpenCV and NLTK to evaluate the reliability of the data and filter out relevant information. The output of this step is a reliable set of information relevant to the user.

[0399] Step 3:

[0400] The server generates a family tree using the obtained relevant information. Here, the extracted information is incorporated into a tree-like data structure and transformed into a visually easy-to-understand format. The generated family tree is the output.

[0401] Step 4:

[0402] The device uses real-time facial expression data collected from the user's visual device to identify the user's emotions. Using facial recognition technology, an emotion engine analyzes what emotions the user is expressing. The output is the identified emotion.

[0403] Step 5:

[0404] The server adjusts the generated family tree and related information based on the identified emotions. It selects relevant information that matches the emotion expressed by the user and generates prompts to display on the smart glasses. The output of this step is information presented according to the emotion.

[0405] Step 6:

[0406] The terminal displays and provides the user with adjusted information on a visual terminal. The display screen shows relevant information and family history. This allows the user to intuitively understand their own history and interesting information. The output is a visual display of information provided to the user.

[0407] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0408] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0409] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0413] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0414] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0415] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0416] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0417] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0418] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0419] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0420] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0421] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0422] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0423] This invention is a data aggregation system designed to enable individuals to automatically generate highly accurate family trees. Based on key information entered by the user through an interface, this system efficiently collects digital data scattered across a network and performs necessary analysis to accurately illustrate family relationships.

[0424] First, the user enters the necessary key information into the system via a terminal. This includes their name, place of origin, and relevant time period. Based on this information, the server automatically searches web databases and collects relevant data from various digital information sources (e.g., old photographs, historical documents, local information, etc.).

[0425] The server applies image analysis techniques to the collected data to extract information about people and families contained in photographs and documents. It also uses natural language processing to analyze specific relationships and historical information from the collected text data. The results of this analysis are evaluated based on their reliability and integrated into a highly reliable dataset within the system.

[0426] This process allows the server to execute an algorithm that generates a family tree from the integrated information. The generated family tree visually shows the relationships between each individual and is displayed on the user's terminal. Furthermore, based on the generated family tree, the server suggests relevant historical information and recommends meaningful tour plans and products to the user.

[0427] For example, if a user attempts to generate a family tree based on information about their great-grandfather, the server can collect relevant religious census records and gravestone information from the great-grandfather's name and place of origin, check its reliability and consistency, and then provide the user with a family history from the great-grandfather to the present. This system allows users to gain a detailed understanding of their roots, discover new family members, and gain insights into historical background.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The user accesses the system interface through a terminal and enters key information for generating the family tree. This includes the names of specific individuals, their places of origin, and relevant time periods.

[0431] Step 2:

[0432] The server automatically searches numerous digital databases on the internet based on the key information entered by the user and collects relevant digital data. This process utilizes search engine APIs and web crawler technologies to extract old photographs, historical documents, and other relevant information.

[0433] Step 3:

[0434] The server applies image analysis algorithms to the collected digital data. This allows it to identify people and objects in photographs and diagrams and perform optical character recognition (OCR) to extract textual information.

[0435] Step 4:

[0436] The server analyzes the collected text data using natural language processing (NLP) techniques. In this process, it identifies keywords that indicate specific family history or historical relationships and understands their meaning.

[0437] Step 5:

[0438] The server calculates a reliability score for the acquired data and determines the reliability of the information based on evaluation criteria. Irrelevant or unreliable information is excluded, and reliable information is integrated to create a dataset.

[0439] Step 6:

[0440] The server uses an AI algorithm to generate a family tree based on the integrated dataset. The family tree visually represents relationships and shows the direct and branch family structure.

[0441] Step 7:

[0442] The server sends the generated family tree to the user's terminal and displays it on the interface. The user can view it, enter additional information as needed, and check related historical information and sightseeing plans suggested by the system.

[0443] Step 8:

[0444] Users can provide feedback based on the options and suggestions offered by the system. Upon receiving the feedback, the server implements a learning process to further improve the accuracy of the data.

[0445] (Example 1)

[0446] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] Traditional methods for generating genealogical information required considerable time and effort to manually collect information from each individual and analyze its relationships. Furthermore, finding reliable data was difficult, and the data was prone to containing misinformation. Additionally, the suggestions for related information based on the generated genealogies were incomplete, making it difficult for users to easily obtain useful knowledge.

[0448] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0449] In this invention, the server includes means for receiving information based on user input, means for automatically acquiring data from a wide-area information network based on key information, and means for performing visual data analysis and natural language analysis on the acquired data and extracting relevant information. As a result, users can quickly and accurately obtain their family history information, and reliable genealogical information can be generated and visualized. Furthermore, useful history-based information can be suggested to the user, improving the efficiency of information retrieval and expanding knowledge.

[0450] "User input" refers to information provided by the user through their device, and it forms the basic data for genealogy generation.

[0451] A "wide-area information network" refers to a place where information is collected, including the internet and digital communication networks, and is the target of data collection.

[0452] "Visual data analysis" is a technology that analyzes image and video data to extract useful information, and includes facial recognition and text recognition.

[0453] "Natural language processing" is a technology that uses algorithms to analyze text data and extract meaning and relationships from it, and includes text mining and content comprehension.

[0454] "Reliability" is a measure used to evaluate the accuracy and relevance of information, and is used in data integration.

[0455] A "genealogical chart" is a diagram that visually represents the relationships between people in a family or group, showing the connections between each individual.

[0456] "Relevant historical information" refers to historical and cultural information suggested based on the generated genealogical chart, intended to deepen the user's understanding.

[0457] This invention is a system designed to allow users to easily generate highly accurate genealogical charts. The system mainly consists of a user terminal and a server, each playing a specific role.

[0458] Users access the system via a terminal. A user interface is provided for entering key information such as personal name, place of origin, and relevant age group. This interface is intuitive, allowing users to easily input the necessary information.

[0459] The entered information is sent to the server. Based on this key information, the server searches databases on a wide-area information network. To achieve this, the server uses a web crawler built with programming languages ​​such as Python or Java. The crawler searches publicly available databases and digital archives on the internet and quickly retrieves relevant data.

[0460] The server performs visual data analysis and natural language processing on the acquired data. For visual data analysis, the open-source image processing library OpenCV is used. The server uses this library to identify individuals from images and photographs and extract relevant information. For natural language processing, the natural language processing toolkit NLTK is used. The server utilizes this to interpret and analyze genealogical information and historical relationships from the collected text data.

[0461] The analyzed information is integrated based on its reliability and formed into a visual genealogical chart using advanced algorithms. This chart is transmitted from the server to the terminal and displayed to the user. In addition, the server has a function to suggest relevant historical information and tourist destinations to the user based on the generated genealogical chart.

[0462] For example, if a user inputs information about their grandfather's name and birthplace into the system, the server will collect and analyze as much detailed family history information as possible based on that information. If a user enters a prompt such as, "Generate a family tree based on my grandfather's name and suggest relevant historical information," the AI ​​can generate appropriate output.

[0463] In this way, one can gain a deep understanding of their own family lineage and its historical background.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The user accesses the system using a terminal and enters key information necessary for generating the family tree. Specifically, they enter their name, place of origin, relevant historical period, etc., into the interface. The entered information is sent directly to the server and stored as initial data. This input information serves as the basis for subsequent data collection and analysis.

[0467] Step 2:

[0468] The server searches databases across a wide-area information network based on key information received from the user. The server efficiently retrieves relevant digital information using a web crawler. This crawler collects data from open data, library archives, and online resources containing historical information. A data search is performed based on the input key information, and relevant data is stored on the server as output.

[0469] Step 3:

[0470] The server performs visual data analysis on the collected data. Using open-source image analysis libraries, it extracts information such as individual faces and names from photographs and images. This step analyzes the visual information obtained from the images and generates output data associated with specific individuals. Specifically, it uses facial recognition technology to identify each person in the photograph.

[0471] Step 4:

[0472] Next, the server performs natural language processing. It analyzes the collected text data using natural language processing tools to identify family relationships and historical context. In this step, language analysis is performed based on the input text data. The output includes information correlations and important historical information. Specifically, it performs text mining to understand family history from the context.

[0473] Step 5:

[0474] The server integrates the information extracted through analysis and evaluates its reliability. It selects highly reliable information and integrates it as a dataset for genealogical tree generation. This integrated data is then used directly as input to the genealogical tree generation algorithm. This reliability evaluation eliminates erroneous information.

[0475] Step 6:

[0476] The genealogy generation algorithm generates a family tree using integrated data. The server executes the algorithm and constructs a genealogy diagram that visually shows each individual and their relationships. The output is generated as a genealogy diagram within the server and finally sent to the terminal. Specifically, it provides a graphical representation using nodes and lines to show individuals and their relationships.

[0477] Step 7:

[0478] The terminal displays the generated genealogy chart to the user. Furthermore, the server suggests historical context and interesting regional information based on the genealogy chart. This allows the user to gain a deeper understanding of their own historical background. The output of this step includes the genealogy chart displayed on the terminal and the suggested information.

[0479] (Application Example 1)

[0480] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0481] Existing genealogy systems only display an individual's historical information, making it difficult to provide a personalized and meaningful customer experience using that information. Furthermore, the inability to recommend relevant products based on highly reliable data prevents maximizing user value. This invention aims to solve this problem by utilizing an individual's genealogical information to suggest products related to their historical background, thereby providing a deeper, more personalized experience.

[0482] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0483] In this invention, the server includes means for automatically collecting data on the network based on key information, means for integrating the collected data and generating a family tree, and means for suggesting relevant products to the user in a virtual store using the generated family tree and associated historical information. This makes it possible for the user to receive product suggestions that utilize the historical background based on their own family lineage.

[0484] "Key information" refers to information provided by users, such as their personal name and place of origin, which serves as the basis for collecting relevant data from the network.

[0485] "Data on the network" refers to a collection of various digital information accessible via the internet, and is the source of the information necessary to generate a family tree.

[0486] "Image analysis" is a technology that uses computers to visually analyze information contained in photographs and documents, and extract information related to specific individuals or families.

[0487] Natural language processing is a technology that enables computers to understand and analyze human language, and is used to identify relationships and historical information from collected text data.

[0488] "Reliability" is an indicator that shows the accuracy and consistency of the collected information, and it is used to judge the value of the information when it is integrated.

[0489] A "family tree" is a diagram that visually shows the blood relationships and family structure between individuals, and is generated based on collected data.

[0490] A "virtual store" is a virtual shop built on the internet, a platform for users to explore and purchase goods online.

[0491] "Related products" are products suggested to users based on their personal family history and background, and their selection takes into account historical and familial context.

[0492] The system for implementing this invention consists of a program that collects and analyzes digital data. Here, we describe a specific form of a virtual store that automatically collects data from a network based on personal information, analyzes it, and then creates a personalized family tree and provides product suggestions based on it.

[0493] The server operates in a high-performance computing environment and collects publicly accessible network data from the internet using "key information" provided by the user. During this process, it utilizes the OpenCV library to perform "image analysis" and extract information related to people and families from image data. Furthermore, it uses the Python NLTK library for "natural language processing" to identify human relationships and historical information from text data. This allows for the evaluation of the "reliability" of the collected information, enabling advanced data integration.

[0494] Based on this integrated information, a "family tree" is constructed, allowing users to trace their roots through it. Furthermore, the "virtual store" displays "related products" associated with this family tree. This allows users to explore products based on their family's historical background and enjoy a personalized shopping experience.

[0495] As a concrete example, a user can input information about their great-grandfather to generate a family tree, and traditional crafts and local specialties related to his birthplace will be showcased in a virtual store. Users can view these items using smart glasses or purchase related products via their smartphone.

[0496] An example of a user entering a prompt using the interface is, "Please view my great-grandfather's family tree and display products related to that historical background." Based on this prompt, the system automatically collects and outputs the relevant information.

[0497] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0498] Step 1:

[0499] Users input "key information," such as their name and place of origin, into the interface via their terminal. This input information becomes the system's basic data.

[0500] Step 2:

[0501] Based on the key information received from the user, the server accesses publicly available databases on the network and automatically collects relevant "data on the network." At this stage, search queries are generated from the database, and the corresponding digital data is aggregated on the server.

[0502] Step 3:

[0503] The server uses the OpenCV library to perform "image analysis" on the collected image data. The input is the collected image data, and by performing face recognition and object detection, it outputs information related to people and families.

[0504] Step 4:

[0505] The server uses the Python NLTK library to perform "natural language processing" on the collected text data. It analyzes relationships and historical information from the input text and extracts specific keywords and phrases. As a result, text information explaining family relationships is output.

[0506] Step 5:

[0507] The server uses the extracted information to evaluate its "reliability." This reliability evaluation takes into account the reliability of the information source and the consistency of the data, and the most reliable information is selected.

[0508] Step 6:

[0509] The server integrates reliable information and generates a "family tree." This family tree is represented by nodes and edges, visually showing the relationships between individual family members. The output is sent to the terminal as a family tree.

[0510] Step 7:

[0511] The user displays a family tree generated on their device and searches for related products through a virtual store. The server selects "related products" based on the family tree and displays them in the virtual store's interface.

[0512] Step 8:

[0513] Users experience relevant products within a virtual store through smart glasses or smartphones. Based on their interest in and willingness to purchase these products, users provide feedback, and the system optimizes its recommendation algorithm based on this feedback.

[0514] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0515] This invention is a system that recognizes the user's emotions and, based on those emotions, generates an optimal family tree and suggests related information. The system is designed to provide information optimized for the user by performing data analysis that combines personal information entered by the user with an emotion engine.

[0516] First, the user accesses the system via a terminal and enters key information necessary to create the family tree. This information includes basic personal details such as name, place of origin, and age. The server uses this information to automatically collect relevant digital data from the network.

[0517] The collected data is analyzed by a server using image analysis and natural language processing technologies. This process extracts relevant information from photographs and documents. Furthermore, reliability algorithms are used during this analysis process, and only reliable information is integrated.

[0518] Next, the server uses an emotion engine to analyze the user's input and responses as they use the system and identify their emotions. This emotion information is taken into consideration in the family tree generation process and in suggesting additional information, providing content that matches the user's emotions. In this way, the system provides the user with the most relevant family tree information and related suggestions, improving the user experience. For example, if the user expresses excitement or joy, the server deepens the user's interest by providing further detailed information and activity suggestions related to these feelings.

[0519] The introduction of this system allows users to better understand their family history and experience greater emotional satisfaction. The system's emotion recognition capabilities play a crucial role in providing users with real-time information and feedback that meets their expectations.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The user accesses the system interface via a terminal and enters the key information necessary to begin generating the family tree. This information includes name, place of origin, and age. This prepares the system to efficiently search for information related to the user's family history.

[0523] Step 2:

[0524] The server searches various data sources on the network based on key information provided by the user and collects relevant digital data. This data includes old photographs, historical documents, and historical records. Data collection is performed automatically by pre-configured search engine APIs and web crawlers.

[0525] Step 3:

[0526] The server performs image analysis on the collected digital data. This process extracts information from photographs and documents using algorithms to identify people and objects. Simultaneously, it applies OCR technology to convert text information within images into text data.

[0527] Step 4:

[0528] The server analyzes the text data obtained in the previous step using natural language processing techniques. Here, it extracts information related to family lineage from sentences and phrases, identifying important keywords and relationships. At this stage, relationships between individuals and historical connections are identified.

[0529] Step 5:

[0530] The server uses an algorithm to determine the reliability of the extracted information and calculates a reliability score for each data point. Uncertain data is eliminated, and only highly reliable information is selected and integrated. This process ensures that users can use accurate information with confidence.

[0531] Step 6:

[0532] The server utilizes an emotion engine to analyze user input and responses, identifying the user's emotional state (joy, surprise, interest, etc.). This emotional information influences the display of generated family trees and suggested additional information, and is used to personalize the user experience.

[0533] Step 7:

[0534] The server integrates reliable information and generates a family tree, taking into account emotional data obtained from the emotion engine. The family tree visually shows relationships and is displayed on the terminal in a format that is easy for the user to understand.

[0535] Step 8:

[0536] Users view the displayed family tree and accompanying information on their device and select additional information suggested by the system based on their interests and feelings. Users can provide feedback, and the system continuously improves its accuracy and user experience based on this feedback.

[0537] (Example 2)

[0538] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0539] In modern society, many people are interested in their family history and roots, but researching them is difficult for individuals due to the complexity of information gathering and organization. Furthermore, it is necessary not only to provide information but also to analyze individual emotions and provide relevant information to improve the user experience. This invention aims to solve this problem by providing a system that efficiently and reliably collects information and enables the generation of a family tree tailored to the user, along with related suggestions.

[0540] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0541] In this invention, the server includes means for automatically collecting information on a communication network based on key information; means for performing image processing and language processing on the collected information and extracting relevant data; means for evaluating the reliability of the extracted data and integrating the highly reliable data; means for constructing a genealogical chart using the integrated data; means for providing additional relevant data based on the constructed genealogical chart; and means for identifying emotions through user input and optimizing additional information based on those emotions. This makes it possible to provide highly reliable and relevant family information while taking into account individual emotions.

[0542] "Key information" refers to basic personal information entered by the user, such as name, place of origin, and age, and serves as the basis for automatically collecting data.

[0543] A "communication network" refers to the entire internet and data network used for sending and receiving information, and is the infrastructure used for acquiring and processing data.

[0544] "Image processing" is a technique that analyzes collected image data and extracts useful information, and includes techniques such as recognizing specific patterns and faces.

[0545] "Language processing" refers to techniques for analyzing text data and extracting meaningful information, and includes natural language processing.

[0546] "Reliability" is an indicator that shows the accuracy and credibility of collected information, and is evaluated based on the source of the information and the results of verification.

[0547] A "genealogical chart" is a diagram that visually represents the family history of a particular family or individual, showing family relationships and generations.

[0548] "Emotions" refer to the feelings and reactions that users exhibit when using a system, and are psychological states analyzed from input data and behavior.

[0549] To implement this invention, the user first accesses the system via a terminal and inputs key information necessary for generating a family tree. This key information includes basic personal information such as name, place of origin, and age. This information serves as the basis for data collection.

[0550] The server automatically collects relevant digital data using the communication network based on the entered key information. The technologies used include web scraping tools to crawl the internet and APIs for data acquisition. Generative AI models are also utilized for natural language processing. For example, the server searches for and retrieves relevant information from genealogical records and photo databases.

[0551] The collected data is analyzed by the server using image processing and natural language processing technologies. For example, facial recognition algorithms may be used for image processing. This allows for the identification of individuals from photographs and the extraction of relevant information. In addition, natural language processing extracts personal names and date information from text. This process utilizes generative AI models to achieve highly accurate information extraction.

[0552] Furthermore, the server evaluates the reliability of the data, selecting and integrating only the most reliable information. The reliability assessment considers factors such as the source of the information and its consistency with other information. For example, information from official records is considered highly reliable.

[0553] When a user uses the system, the server analyzes the user's input and actions using an emotion engine to identify the user's emotions. This emotion information is then used to generate the optimal family tree and suggest relevant information. Specifically, when a user expresses enjoyment, information about episodes and related events that match that emotion is presented.

[0554] Finally, an example of a prompt is given: "Build an algorithm that provides highly relevant genealogical information and suggestions based on the user's input and emotions. The user has entered their grandfather's name and place of origin. Optimize the analysis of relevant historical documents and photographs based on reliability, and suggest detailed information about local history if emotions are confirmed." This prompt serves as the foundation for improving the user experience using a generative AI model.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] Users access the system via a terminal and enter key information such as their name, place of origin, and age. This input serves as the basis for data collection.

[0558] Step 2:

[0559] The server automatically collects relevant digital data using the communication network based on the key information entered by the user. Here, web scraping tools and APIs are used to explore online genealogical records and photo databases. The input is the user's key information, and the output is the collected dataset.

[0560] Step 3:

[0561] The server performs image processing and language processing on the collected data and extracts relevant data. Specifically, it uses facial recognition technology for image processing and a generative AI model for language processing. The input to this process is the collected data from the previous step, and the output is the extracted target information.

[0562] Step 4:

[0563] The server evaluates the reliability of the extracted data. To evaluate reliability, the source of the data is verified and consistency is checked. The input here is the extracted information, and the output is reliable data.

[0564] Step 5:

[0565] The server integrates reliable data and generates a genealogical chart. Data integration organizes relevant information based on chronological order and relationships, creating a visualized genealogical chart. The input to this process is reliable extracted data, and the output is the generated genealogical chart.

[0566] Step 6:

[0567] The server identifies the user's emotions using an emotion engine. It analyzes the user's interaction data and input. The input for this step is the user's response data, and the output is the identified emotion information.

[0568] Step 7:

[0569] The server provides additional information and suggestions tailored to the user's emotions based on the genealogy diagram. Emotion-based information optimization includes presenting relevant events and episodes. The input for this step is the generated genealogy diagram and emotion information, and the output is optimized suggestion information.

[0570] (Application Example 2)

[0571] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0572] In today's information environment, users have access to a vast amount of information, but finding it useful and interesting is difficult. In particular, information related to family history and genealogy can lead to misunderstandings if it lacks reliability or relevance. Furthermore, methods for providing personalized information based on user emotions are limited. There is a need to solve these problems and enable users to easily find relevant information and develop an interest in it.

[0573] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0574] In this invention, the server includes means for automatically collecting data on the network based on key information, means for performing image analysis and natural language processing on the collected data to extract relevant information, and means for determining the reliability of the extracted information and integrating the reliable information. This enables users to efficiently acquire information that is highly relevant and reliable to them, thereby improving their psychological satisfaction.

[0575] "Key information" refers to personal information entered by users, such as their name, place of origin, and age, and forms the basis for data collection and analysis.

[0576] "Network data" refers to a collection of digital information accessible through the internet or cloud services, and serves as a source of information for generating family trees.

[0577] "Image analysis" refers to techniques for extracting useful information from collected digital images, and is a method that improves the reliability of information when combined with natural language processing.

[0578] "Natural language processing" refers to techniques for understanding meaning from collected document data, and is used in combination with image analysis to extract relevant information.

[0579] "Related information" refers to data useful for generating family trees and suggesting information to users, and is reliable and integrated information.

[0580] "Identifying emotions" refers to the process of identifying a user's psychological state at a given moment based on their input and responses.

[0581] "Visual devices" refer to display devices such as smart glasses, which are devices used to display information to the user.

[0582] The embodiments for carrying out the invention are described below.

[0583] To implement this invention, the user must wear a dedicated visual terminal, such as smart glasses. The user inputs key information via the visual terminal. This key information includes the user's personal information, such as name, place of origin, and age. The server then automatically collects data from the network.

[0584] The server attempts to analyze the collected data using image analysis techniques (e.g., libraries such as OpenCV) and natural language processing techniques (such as NLTK). This analysis involves extracting information from digital images and understanding the meaning of document data, thereby extracting highly reliable relevant information. Reliability is determined by calculating a reliability score for the data.

[0585] Furthermore, the server uses emotion recognition technology to identify the user's emotions when using the visual device. This emotion data influences the generation of the family tree, and relevant information is also adjusted according to the user's emotions. Finally, the necessary information is displayed on the smart glasses and provided visually based on the user's emotions.

[0586] For example, when a user expresses interest, relevant family history information and past events are displayed in accordance with that emotion, amplifying the user's interest.

[0587] An example of a prompt to input into a generative AI model would be: "Identify emotions from this user's facial expression data and provide relevant family information in real time. The user's name is a common name, and they are interested in past history."

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The server receives key information entered by the user. Based on the entered information such as name, place of origin, and age, it accesses publicly available databases and cloud resources on the internet and automatically collects relevant data. The output at this stage is a collection of digital data related to the individual user.

[0591] Step 2:

[0592] The server performs image analysis and natural language processing on the collected data. It recognizes people and specific symbols from image data and extracts relevant text information from document data. It uses OpenCV and NLTK to evaluate the reliability of the data and filter out relevant information. The output of this step is a reliable set of information relevant to the user.

[0593] Step 3:

[0594] The server generates a family tree using the obtained relevant information. Here, the extracted information is incorporated into a tree-like data structure and transformed into a visually easy-to-understand format. The generated family tree is the output.

[0595] Step 4:

[0596] The device uses real-time facial expression data collected from the user's visual device to identify the user's emotions. Using facial expression recognition technology, an emotion engine analyzes what emotions the user is expressing. The output is the identified emotion.

[0597] Step 5:

[0598] The server adjusts the generated family tree and related information based on the identified emotions. It selects relevant information that matches the emotion expressed by the user and generates prompts to display on the smart glasses. The output of this step is information presented according to the emotion.

[0599] Step 6:

[0600] The terminal displays and provides the user with adjusted information on a visual terminal. The display screen shows relevant information and family history. This allows the user to intuitively understand their own history and interesting information. The output is a visual display of information provided to the user.

[0601] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0602] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0603] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0607] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0608] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0609] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0610] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0611] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0612] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0613] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0614] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0615] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0616] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0617] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] This invention is a data aggregation system designed to enable individuals to automatically generate highly accurate family trees. Based on key information entered by the user through an interface, this system efficiently collects digital data scattered across a network and performs necessary analysis to accurately illustrate family relationships.

[0619] First, the user enters the necessary key information into the system via a terminal. This includes their name, place of origin, and relevant time period. Based on this information, the server automatically searches web databases and collects relevant data from various digital information sources (e.g., old photographs, historical documents, local information, etc.).

[0620] The server applies image analysis techniques to the collected data to extract information about people and families contained in photographs and documents. It also uses natural language processing to analyze specific relationships and historical information from the collected text data. The results of this analysis are evaluated based on their reliability and integrated into a highly reliable dataset within the system.

[0621] This process allows the server to execute an algorithm that generates a family tree from the integrated information. The generated family tree visually shows the relationships between each individual and is displayed on the user's terminal. Furthermore, based on the generated family tree, the server suggests relevant historical information and recommends meaningful tour plans and products to the user.

[0622] For example, if a user attempts to generate a family tree based on information about their great-grandfather, the server can collect relevant religious census records and gravestone information from the great-grandfather's name and place of origin, check its reliability and consistency, and then provide the user with a family history from the great-grandfather to the present. This system allows users to gain a detailed understanding of their roots, discover new family members, and gain insights into historical background.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The user accesses the system interface through a terminal and enters key information for generating the family tree. This includes the names of specific individuals, their places of origin, and relevant time periods.

[0626] Step 2:

[0627] The server automatically searches numerous digital databases on the internet based on the key information entered by the user and collects relevant digital data. This process utilizes search engine APIs and web crawler technologies to extract old photographs, historical documents, and other relevant information.

[0628] Step 3:

[0629] The server applies image analysis algorithms to the collected digital data. This allows it to identify people and objects in photographs and diagrams and perform optical character recognition (OCR) to extract textual information.

[0630] Step 4:

[0631] The server analyzes the collected text data using natural language processing (NLP) techniques. In this process, it identifies keywords that indicate specific family history or historical relationships and understands their meaning.

[0632] Step 5:

[0633] The server calculates a reliability score for the acquired data and determines the reliability of the information based on evaluation criteria. Irrelevant or unreliable information is excluded, and reliable information is integrated to create a dataset.

[0634] Step 6:

[0635] The server uses an AI algorithm to generate a family tree based on the integrated dataset. The family tree visually represents relationships and shows the direct and branch family structure.

[0636] Step 7:

[0637] The server sends the generated family tree to the user's terminal and displays it on the interface. The user can view it, enter additional information as needed, and check related historical information and sightseeing plans suggested by the system.

[0638] Step 8:

[0639] Users can provide feedback based on the options and suggestions offered by the system. Upon receiving the feedback, the server implements a learning process to further improve the accuracy of the data.

[0640] (Example 1)

[0641] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0642] Traditional methods for generating genealogical information required considerable time and effort to manually collect information from each individual and analyze its relationships. Furthermore, finding reliable data was difficult, and the data was prone to containing misinformation. Additionally, the suggestions for related information based on the generated genealogies were incomplete, making it difficult for users to easily obtain useful knowledge.

[0643] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0644] In this invention, the server includes means for receiving information based on user input, means for automatically acquiring data from a wide-area information network based on key information, and means for performing visual data analysis and natural language analysis on the acquired data and extracting relevant information. As a result, users can quickly and accurately obtain their family history information, and reliable genealogical information can be generated and visualized. Furthermore, useful history-based information can be suggested to the user, improving the efficiency of information retrieval and expanding knowledge.

[0645] "User input" refers to information provided by the user through their device, and it forms the basic data for genealogy generation.

[0646] A "wide-area information network" refers to a place where information is collected, including the internet and digital communication networks, and is the target of data collection.

[0647] "Visual data analysis" is a technology that analyzes image and video data to extract useful information, and includes facial recognition and text recognition.

[0648] "Natural language processing" is a technology that uses algorithms to analyze text data and extract meaning and relationships from it, and includes text mining and content comprehension.

[0649] "Reliability" is a measure used to evaluate the accuracy and relevance of information, and is used in data integration.

[0650] A "genealogical chart" is a diagram that visually represents the relationships between people in a family or group, showing the connections between each individual.

[0651] "Relevant historical information" refers to historical and cultural information suggested based on the generated genealogical chart, intended to deepen the user's understanding.

[0652] This invention is a system designed to allow users to easily generate highly accurate genealogical charts. The system mainly consists of a user terminal and a server, each playing a specific role.

[0653] Users access the system via a terminal. A user interface is provided for entering key information such as personal name, place of origin, and relevant age group. This interface is intuitive, allowing users to easily input the necessary information.

[0654] The entered information is sent to the server. Based on this key information, the server searches databases on a wide-area information network. To achieve this, the server uses a web crawler built with programming languages ​​such as Python or Java. The crawler searches publicly available databases and digital archives on the internet and quickly retrieves relevant data.

[0655] The server performs visual data analysis and natural language processing on the acquired data. For visual data analysis, the open-source image processing library OpenCV is used. The server uses this library to identify individuals from images and photographs and extract relevant information. For natural language processing, the natural language processing toolkit NLTK is used. The server utilizes this to interpret and analyze genealogical information and historical relationships from the collected text data.

[0656] The analyzed information is integrated based on its reliability and formed into a visual genealogical chart using advanced algorithms. This chart is transmitted from the server to the terminal and displayed to the user. In addition, the server has a function to suggest relevant historical information and tourist destinations to the user based on the generated genealogical chart.

[0657] For example, if a user inputs information about their grandfather's name and birthplace into the system, the server will collect and analyze as much detailed family history information as possible based on that information. If a user enters a prompt such as, "Generate a family tree based on my grandfather's name and suggest relevant historical information," the AI ​​can generate appropriate output.

[0658] In this way, one can gain a deep understanding of their own family lineage and its historical background.

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] The user accesses the system using a terminal and enters key information necessary for generating the family tree. Specifically, they enter their name, place of origin, relevant historical period, etc., into the interface. The entered information is sent directly to the server and stored as initial data. This input information serves as the basis for subsequent data collection and analysis.

[0662] Step 2:

[0663] The server searches databases across a wide-area information network based on key information received from the user. The server efficiently retrieves relevant digital information using a web crawler. This crawler collects data from open data, library archives, and online resources containing historical information. A data search is performed based on the input key information, and relevant data is stored on the server as output.

[0664] Step 3:

[0665] The server performs visual data analysis on the collected data. Using open-source image analysis libraries, it extracts information such as individual faces and names from photographs and images. This step analyzes the visual information obtained from the images and generates output data associated with specific individuals. Specifically, it uses facial recognition technology to identify each person in the photograph.

[0666] Step 4:

[0667] Next, the server performs natural language processing. It analyzes the collected text data using natural language processing tools to identify family relationships and historical context. In this step, language analysis is performed based on the input text data. The output includes information correlations and important historical information. Specifically, it performs text mining to understand family history from the context.

[0668] Step 5:

[0669] The server integrates the information extracted through analysis and evaluates its reliability. It selects highly reliable information and integrates it as a dataset for genealogical tree generation. This integrated data is then used directly as input to the genealogical tree generation algorithm. This reliability evaluation eliminates erroneous information.

[0670] Step 6:

[0671] The genealogy generation algorithm generates a family tree using integrated data. The server executes the algorithm and constructs a genealogy diagram that visually shows each individual and their relationships. The output is generated as a genealogy diagram within the server and finally sent to the terminal. Specifically, it provides a graphical representation using nodes and lines to show individuals and their relationships.

[0672] Step 7:

[0673] The terminal displays the generated genealogy chart to the user. Furthermore, the server suggests historical context and interesting regional information based on the genealogy chart. This allows the user to gain a deeper understanding of their own historical background. The output of this step includes the genealogy chart displayed on the terminal and the suggested information.

[0674] (Application Example 1)

[0675] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0676] Existing genealogy systems only display an individual's historical information, making it difficult to provide a personalized and meaningful customer experience using that information. Furthermore, the inability to recommend relevant products based on highly reliable data prevents maximizing user value. This invention aims to solve this problem by utilizing an individual's genealogical information to suggest products related to their historical background, thereby providing a deeper, more personalized experience.

[0677] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0678] In this invention, the server includes means for automatically collecting data on the network based on key information, means for integrating the collected data and generating a family tree, and means for suggesting relevant products to the user in a virtual store using the generated family tree and associated historical information. This makes it possible for the user to receive product suggestions that utilize the historical background based on their own family lineage.

[0679] "Key information" refers to information provided by users, such as their personal name and place of origin, which serves as the basis for collecting relevant data from the network.

[0680] "Data on the network" refers to a collection of various digital information accessible via the internet, and is the source of the information necessary to generate a family tree.

[0681] "Image analysis" is a technology that uses computers to visually analyze information contained in photographs and documents, and extract information related to specific individuals or families.

[0682] Natural language processing is a technology that enables computers to understand and analyze human language, and is used to identify relationships and historical information from collected text data.

[0683] "Reliability" is an indicator that shows the accuracy and consistency of the collected information, and it is used to judge the value of the information when it is integrated.

[0684] A "family tree" is a diagram that visually shows the blood relationships and family structure between individuals, and is generated based on collected data.

[0685] A "virtual store" is a virtual shop built on the internet, a platform for users to explore and purchase goods online.

[0686] "Related products" are products suggested to users based on their personal family history and background, and their selection takes into account historical and familial context.

[0687] The system for implementing this invention consists of a program that collects and analyzes digital data. Here, we describe a specific form of a virtual store that automatically collects data from a network based on personal information, analyzes it, and then creates a personalized family tree and provides product suggestions based on it.

[0688] The server operates in a high-performance computing environment and collects publicly accessible network data from the internet using "key information" provided by the user. During this process, it utilizes the OpenCV library to perform "image analysis" and extract information related to people and families from image data. Furthermore, it uses the Python NLTK library for "natural language processing" to identify human relationships and historical information from text data. This allows for the evaluation of the "reliability" of the collected information, enabling advanced data integration.

[0689] Based on this integrated information, a "family tree" is constructed, allowing users to trace their roots through it. Furthermore, the "virtual store" displays "related products" associated with this family tree. This allows users to explore products based on their family's historical background and enjoy a personalized shopping experience.

[0690] As a concrete example, a user can input information about their great-grandfather to generate a family tree, and traditional crafts and local specialties related to his birthplace will be showcased in a virtual store. Users can view these items using smart glasses or purchase related products via their smartphone.

[0691] An example of a user entering a prompt using the interface is, "Please view my great-grandfather's family tree and display products related to that historical background." Based on this prompt, the system automatically collects and outputs the relevant information.

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] Users input "key information," such as their name and place of origin, into the interface via their terminal. This input information becomes the system's basic data.

[0695] Step 2:

[0696] Based on the key information received from the user, the server accesses publicly available databases on the network and automatically collects relevant "data on the network." At this stage, search queries are generated from the database, and the corresponding digital data is aggregated on the server.

[0697] Step 3:

[0698] The server uses the OpenCV library to perform "image analysis" on the collected image data. The input is the collected image data, and by performing face recognition and object detection, it outputs information related to people and families.

[0699] Step 4:

[0700] The server uses the Python NLTK library to perform "natural language processing" on the collected text data. It analyzes relationships and historical information from the input text and extracts specific keywords and phrases. As a result, text information explaining family relationships is output.

[0701] Step 5:

[0702] The server uses the extracted information to evaluate its "reliability." This reliability evaluation takes into account the reliability of the information source and the consistency of the data, and the most reliable information is selected.

[0703] Step 6:

[0704] The server integrates reliable information and generates a "family tree." This family tree is represented by nodes and edges, visually showing the relationships between individual family members. The output is sent to the terminal as a family tree.

[0705] Step 7:

[0706] The user displays a family tree generated on their device and searches for related products through a virtual store. The server selects "related products" based on the family tree and displays them in the virtual store's interface.

[0707] Step 8:

[0708] Users experience relevant products within a virtual store through smart glasses or smartphones. Based on their interest in and willingness to purchase these products, users provide feedback, and the system optimizes its recommendation algorithm based on this feedback.

[0709] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0710] This invention is a system that recognizes the user's emotions and, based on those emotions, generates an optimal family tree and suggests related information. The system is designed to provide information optimized for the user by performing data analysis that combines personal information entered by the user with an emotion engine.

[0711] First, the user accesses the system via a terminal and enters key information necessary to create the family tree. This information includes basic personal details such as name, place of origin, and age. The server uses this information to automatically collect relevant digital data from the network.

[0712] The collected data is analyzed by a server using image analysis and natural language processing technologies. This process extracts relevant information from photographs and documents. Furthermore, reliability algorithms are used during this analysis process, and only reliable information is integrated.

[0713] Next, the server uses an emotion engine to analyze the user's input and responses as they use the system and identify their emotions. This emotion information is taken into consideration in the family tree generation process and in suggesting additional information, providing content that matches the user's emotions. In this way, the system provides the user with the most relevant family tree information and related suggestions, improving the user experience. For example, if the user expresses excitement or joy, the server deepens the user's interest by providing further detailed information and activity suggestions related to these feelings.

[0714] The introduction of this system allows users to better understand their family history and experience greater emotional satisfaction. The system's emotion recognition capabilities play a crucial role in providing users with real-time information and feedback that meets their expectations.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The user accesses the system interface via a terminal and enters the key information necessary to begin generating the family tree. This information includes name, place of origin, and age. This prepares the system to efficiently search for information related to the user's family history.

[0718] Step 2:

[0719] The server searches various data sources on the network based on key information provided by the user and collects relevant digital data. This data includes old photographs, historical documents, and historical records. Data collection is performed automatically by pre-configured search engine APIs and web crawlers.

[0720] Step 3:

[0721] The server performs image analysis on the collected digital data. This process extracts information from photographs and documents using algorithms to identify people and objects. Simultaneously, it applies OCR technology to convert text information within images into text data.

[0722] Step 4:

[0723] The server analyzes the text data obtained in the previous step using natural language processing techniques. Here, it extracts information related to family lineage from sentences and phrases, identifying important keywords and relationships. At this stage, relationships between individuals and historical connections are identified.

[0724] Step 5:

[0725] The server uses an algorithm to determine the reliability of the extracted information and calculates a reliability score for each data point. Uncertain data is eliminated, and only highly reliable information is selected and integrated. This process ensures that users can use accurate information with confidence.

[0726] Step 6:

[0727] The server utilizes an emotion engine to analyze user input and responses, identifying the user's emotional state (joy, surprise, interest, etc.). This emotional information influences the display of generated family trees and suggested additional information, and is used to personalize the user experience.

[0728] Step 7:

[0729] The server integrates reliable information and generates a family tree, taking into account emotional data obtained from the emotion engine. The family tree visually shows relationships and is displayed on the terminal in a format that is easy for the user to understand.

[0730] Step 8:

[0731] Users view the displayed family tree and accompanying information on their device and select additional information suggested by the system based on their interests and feelings. Users can provide feedback, and the system continuously improves its accuracy and user experience based on this feedback.

[0732] (Example 2)

[0733] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] In modern society, many people are interested in their family history and roots, but researching them is difficult for individuals due to the complexity of information gathering and organization. Furthermore, it is necessary not only to provide information but also to analyze individual emotions and provide relevant information to improve the user experience. This invention aims to solve this problem by providing a system that efficiently and reliably collects information and enables the generation of a family tree tailored to the user, along with related suggestions.

[0735] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0736] In this invention, the server includes means for automatically collecting information on a communication network based on key information; means for performing image processing and language processing on the collected information and extracting relevant data; means for evaluating the reliability of the extracted data and integrating the highly reliable data; means for constructing a genealogical chart using the integrated data; means for providing additional relevant data based on the constructed genealogical chart; and means for identifying emotions through user input and optimizing additional information based on those emotions. This makes it possible to provide highly reliable and relevant family information while taking into account individual emotions.

[0737] "Key information" refers to basic personal information entered by the user, such as name, place of origin, and age, and serves as the basis for automatically collecting data.

[0738] A "communication network" refers to the entire internet and data network used for sending and receiving information, and is the infrastructure used for acquiring and processing data.

[0739] "Image processing" is a technique that analyzes collected image data and extracts useful information, and includes techniques such as recognizing specific patterns and faces.

[0740] "Language processing" refers to techniques for analyzing text data and extracting meaningful information, and includes natural language processing.

[0741] "Reliability" is an indicator that shows the accuracy and credibility of collected information, and is evaluated based on the source of the information and the results of verification.

[0742] A "genealogical chart" is a diagram that visually represents the family history of a particular family or individual, showing family relationships and generations.

[0743] "Emotions" refer to the feelings and reactions that users exhibit when using a system, and are psychological states analyzed from input data and behavior.

[0744] To implement this invention, the user first accesses the system via a terminal and inputs key information necessary for generating a family tree. This key information includes basic personal information such as name, place of origin, and age. This information serves as the basis for data collection.

[0745] The server automatically collects relevant digital data using the communication network based on the entered key information. The technologies used include web scraping tools to crawl the internet and APIs for data acquisition. Generative AI models are also utilized for natural language processing. For example, the server searches for and retrieves relevant information from genealogical records and photo databases.

[0746] The collected data is analyzed by the server using image processing and natural language processing technologies. For example, facial recognition algorithms may be used for image processing. This allows for the identification of individuals from photographs and the extraction of relevant information. In addition, natural language processing extracts personal names and date information from text. This process utilizes generative AI models to achieve highly accurate information extraction.

[0747] Furthermore, the server evaluates the reliability of the data, selecting and integrating only the most reliable information. The reliability assessment considers factors such as the source of the information and its consistency with other information. For example, information from official records is considered highly reliable.

[0748] When a user uses the system, the server analyzes the user's input and actions using an emotion engine to identify the user's emotions. This emotion information is then used to generate the optimal family tree and suggest relevant information. Specifically, when a user expresses enjoyment, information about episodes and related events that match that emotion is presented.

[0749] Finally, an example of a prompt is given: "Build an algorithm that provides highly relevant genealogical information and suggestions based on the user's input and emotions. The user has entered their grandfather's name and place of origin. Optimize the analysis of relevant historical documents and photographs based on reliability, and suggest detailed information about local history if emotions are confirmed." This prompt serves as the foundation for improving the user experience using a generative AI model.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] Users access the system via a terminal and enter key information such as their name, place of origin, and age. This input serves as the basis for data collection.

[0753] Step 2:

[0754] The server automatically collects relevant digital data using the communication network based on the key information entered by the user. Here, web scraping tools and APIs are used to explore online genealogical records and photo databases. The input is the user's key information, and the output is the collected dataset.

[0755] Step 3:

[0756] The server performs image processing and language processing on the collected data and extracts relevant data. Specifically, it uses facial recognition technology for image processing and a generative AI model for language processing. The input to this process is the collected data from the previous step, and the output is the extracted target information.

[0757] Step 4:

[0758] The server evaluates the reliability of the extracted data. To evaluate reliability, the source of the data is verified and consistency is checked. The input here is the extracted information, and the output is reliable data.

[0759] Step 5:

[0760] The server integrates reliable data and generates a genealogical chart. Data integration organizes relevant information based on chronological order and relationships, creating a visualized genealogical chart. The input to this process is reliable extracted data, and the output is the generated genealogical chart.

[0761] Step 6:

[0762] The server identifies the user's emotions using an emotion engine. It analyzes the user's interaction data and input. The input for this step is the user's response data, and the output is the identified emotion information.

[0763] Step 7:

[0764] The server provides additional information and suggestions tailored to the user's emotions based on the genealogy diagram. Emotion-based information optimization includes presenting relevant events and episodes. The input for this step is the generated genealogy diagram and emotion information, and the output is optimized suggestion information.

[0765] (Application Example 2)

[0766] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0767] In today's information environment, users have access to a vast amount of information, but finding it useful and interesting is difficult. In particular, information related to family history and genealogy can lead to misunderstandings if it lacks reliability or relevance. Furthermore, methods for providing personalized information based on user emotions are limited. There is a need to solve these problems and enable users to easily find relevant information and develop an interest in it.

[0768] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0769] In this invention, the server includes means for automatically collecting data on the network based on key information, means for performing image analysis and natural language processing on the collected data to extract relevant information, and means for determining the reliability of the extracted information and integrating the reliable information. This enables users to efficiently acquire information that is highly relevant and reliable to them, thereby improving their psychological satisfaction.

[0770] "Key information" refers to personal information entered by users, such as their name, place of origin, and age, and forms the basis for data collection and analysis.

[0771] "Network data" refers to a collection of digital information accessible through the internet or cloud services, and serves as a source of information for generating family trees.

[0772] "Image analysis" refers to techniques for extracting useful information from collected digital images, and is a method that improves the reliability of information when combined with natural language processing.

[0773] "Natural language processing" refers to techniques for understanding meaning from collected document data, and is used in combination with image analysis to extract relevant information.

[0774] "Related information" refers to data useful for generating family trees and suggesting information to users, and is reliable and integrated information.

[0775] "Identifying emotions" refers to the process of identifying a user's psychological state at a given moment based on their input and responses.

[0776] "Visual devices" refer to display devices such as smart glasses, which are devices used to display information to the user.

[0777] The embodiments for carrying out the invention are described below.

[0778] To implement this invention, the user must wear a dedicated visual terminal, such as smart glasses. The user inputs key information via the visual terminal. This key information includes the user's personal information, such as name, place of origin, and age. The server then automatically collects data from the network.

[0779] The server attempts to analyze the collected data using image analysis techniques (e.g., libraries such as OpenCV) and natural language processing techniques (such as NLTK). This analysis involves extracting information from digital images and understanding the meaning of document data, thereby extracting highly reliable relevant information. Reliability is determined by calculating a reliability score for the data.

[0780] Furthermore, the server uses emotion recognition technology to identify the user's emotions when using the visual device. This emotion data influences the generation of the family tree, and relevant information is also adjusted according to the user's emotions. Finally, the necessary information is displayed on the smart glasses and provided visually based on the user's emotions.

[0781] For example, when a user expresses interest, relevant family history information and past events are displayed in accordance with that emotion, amplifying the user's interest.

[0782] An example of a prompt to input into a generative AI model would be: "Identify emotions from this user's facial expression data and provide relevant family information in real time. The user's name is a common name, and they are interested in past history."

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The server receives key information entered by the user. Based on the entered information such as name, place of origin, and age, it accesses publicly available databases and cloud resources on the internet and automatically collects relevant data. The output at this stage is a collection of digital data related to the individual user.

[0786] Step 2:

[0787] The server performs image analysis and natural language processing on the collected data. It recognizes people and specific symbols from image data and extracts relevant text information from document data. It uses OpenCV and NLTK to evaluate the reliability of the data and filter out relevant information. The output of this step is a reliable set of information relevant to the user.

[0788] Step 3:

[0789] The server generates a family tree using the obtained relevant information. Here, the extracted information is incorporated into a tree-like data structure and transformed into a visually easy-to-understand format. The generated family tree is the output.

[0790] Step 4:

[0791] The device uses real-time facial expression data collected from the user's visual device to identify the user's emotions. Using facial expression recognition technology, an emotion engine analyzes what emotions the user is expressing. The output is the identified emotion.

[0792] Step 5:

[0793] The server adjusts the generated family tree and related information based on the identified emotions. It selects relevant information that matches the emotion expressed by the user and generates prompts to display on the smart glasses. The output of this step is information presented according to the emotion.

[0794] Step 6:

[0795] The terminal displays and provides the user with adjusted information on a visual terminal. The display screen shows relevant information and family history. This allows the user to intuitively understand their own history and interesting information. The output is a visual display of information provided to the user.

[0796] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0797] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0798] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0799] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0800] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0801] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0802] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0803] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0804] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0805] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0806] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0807] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0808] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0809] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0810] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0811] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0812] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0813] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0814] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0815] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0816] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0817] The following is further disclosed regarding the embodiments described above.

[0818] (Claim 1)

[0819] A means for automatically collecting data on a network based on key information,

[0820] A means of performing image analysis and natural language processing on collected data to extract relevant information,

[0821] A means for determining the reliability of extracted information and integrating highly reliable information,

[0822] A means of generating a family tree using integrated information,

[0823] A means of suggesting additional relevant information based on the generated family tree,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, comprising means for calculating a reliability score for collected data and filtering information based thereon.

[0827] (Claim 3)

[0828] The system according to claim 1, comprising means for displaying the generated family tree and related information on a user terminal and for inputting feedback to the user.

[0829] "Example 1"

[0830] (Claim 1)

[0831] A means of receiving information based on user input,

[0832] A means for automatically acquiring data on a wide-area information network based on key information,

[0833] A means of performing visual data analysis and natural language processing on acquired data to extract relevant information,

[0834] A means to judge the reliability of the extracted information and integrate the highly reliable information,

[0835] A means for generating a genealogical chart using integrated information,

[0836] A means of providing relevant historical information and suggestions based on the generated genealogical chart,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which calculates a confidence index for acquired data and selects information based on it.

[0840] (Claim 3)

[0841] The system according to claim 1, further comprising means for outputting the generated genealogy chart and related information to a user terminal and for inputting opinions to the user.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] A means for automatically collecting data on a network based on key information,

[0845] A means of performing image analysis and natural language processing on collected data to extract relevant information,

[0846] A means for determining the reliability of extracted information and integrating highly reliable information,

[0847] A means of generating a family tree using integrated information,

[0848] A method for suggesting related products to users in a virtual store using the generated family tree and associated historical information,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, comprising means for calculating a reliability score for collected data, filtering information based on the reliability score, and reflecting the filtered information in product recommendations for a virtual store.

[0852] (Claim 3)

[0853] The system according to claim 1, comprising means for displaying the generated family tree and related information on a user terminal, for inputting feedback from the user, and for customizing the purchasing experience in a virtual store.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] A means for automatically collecting information on a communication network based on key information,

[0857] A means for performing image processing and language processing on collected information and extracting relevant data,

[0858] A means to evaluate the reliability of extracted data and integrate highly reliable data,

[0859] A means of constructing a genealogical chart using integrated data,

[0860] A means of providing additional related data based on the constructed genealogical chart,

[0861] A means for identifying emotions based on user input and optimizing additional information based on those emotions,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, comprising means for calculating a confidence score of collected information and selecting data based on that score.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising means for presenting a constructed genealogy chart and related information to a user device and prompting the user to input a response.

[0867] "Application example 2 when combining with an emotional engine"

[0868] (Claim 1)

[0869] A means for automatically collecting data on a network based on key information,

[0870] A means of performing image analysis and natural language processing on collected data to extract relevant information,

[0871] A means for determining the reliability of extracted information and integrating highly reliable information,

[0872] A means of generating a family tree using integrated information,

[0873] A means of suggesting additional relevant information based on the generated family tree,

[0874] A means for identifying the user's emotions and adjusting relevant information based on the identified emotions,

[0875] A means of providing additional information through a visual device for displaying information that responds to emotions,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, comprising means for calculating a reliability score for collected data and filtering information based thereon.

[0879] (Claim 3)

[0880] The system according to claim 1, comprising means for displaying the generated family tree and related information on a visual terminal and for inputting feedback to the user. [Explanation of Symbols]

[0881] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for automatically collecting data on a network based on key information, A means of performing image analysis and natural language processing on collected data to extract relevant information, A means for determining the reliability of extracted information and integrating highly reliable information, A means of generating a family tree using integrated information, A means of suggesting additional relevant information based on the generated family tree, A system that includes this.

2. The system according to claim 1, comprising means for calculating a reliability score for collected data and filtering information based thereon.

3. The system according to claim 1, comprising means for displaying the generated family tree and related information on a user terminal and for inputting feedback to the user.

Images